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The CIO’s Guide to Artificial Intelligence

CIOs can separate AI hype from reality by considering these areas of risk and opportunity.

When a company realized that up to 30% of calls it received were from customers asking about order status, its leadership wanted to know if artificial intelligence (AI) would be able to help manage the interactions. The short answer was yes, a virtual customer assistant could answer questions ranging from “Where is my order” to “How long will I have to wait?” But the bigger question was if AI could help the company in even more impactful ways.

“Look at how you are using technology today during critical interactions with customers — business moments — and consider how the value of that moment could be increased,” says Whit Andrews, vice president and distinguished analyst at Gartner. “Then apply AI to those points for additional business value.”

AI allows companies to collect data from a wide variety of places and apply self-improving analysis that can take action

For example, the interaction between company and consumer provides data about the customer. When combining information with other data about that particular customer (i.e., they order X amount of Y products every Z weeks), the company can use AI to further enrich the relationship beyond that interaction.

During future interactions, the data might allow the seller to ask questions specific to the customer, such as “We know you are frequently waiting on delivery. Would you like to subscribe to this product or order larger quantities?” AI enables companies to collect data from a wide variety of places and apply self-improving analysis that can take action — and on a level of granularity never before available.

Key insights for CIOs

“Savvy CIOs are experimenting jointly with business peers to discover top use cases for AI to evaluate its potential to disrupt markets and remake existing business models,” says Janelle B. Hill, vice president and distinguished analyst at Gartner.

Here are three key insights for CIOs to know before they start a successful AI journey.

Digital business is accelerating interest in AI at a pace that has left many CIOs hurrying to build an AI strategy and investment plan appropriate for their enterprise.Over the past few years, the pace of innovation in AI technologies has been staggering, predominantly coming from small vendors. CIOs are in the perfect position to educate their company’s CEO and board about recent developments in AI and illustrate how AI might influence their business and competitive landscape. By following this approach, CIOs can potentially flip the traditional engagement model between IT and the business, influencing business strategy at the outset, rather than simply developing implementation projects that follow up on the executive team’s decisions.

Deep learning, natural-language processing (NLP) and computer vision are leading areas of rapid technology advancement, and are the areas where CIOs need to build knowledge, expertise and skills.Recent breakthroughs in machine learning, big data, computer vision and speech recognition are increasing the commercial potential of AI. But AI requires new skills and a new way of thinking about problems. CIOs must ensure that IT owns the strategy and governance of AI solutions. Although pilot AI experiments can start with a small investment, for full production rollout, the biggest area of investment is building and retaining the necessary talent. These skills include technical knowledge in specific AI technologies, data science, maintaining quality data, problem domain expertise, and skills to monitor, maintain and govern the environment.

Market conditions for commercial success with AI technology are well-aligned, making AI safe enough for CIOs to investigate, experiment with and strategize about potential application use cases.Capabilities like voice recognition, NLP and image processing benefit from advances in big data processing and advanced analytical methods such as machine learning and deep learning. Leading-edge AI technologies will play an increasingly important role in the top three business objectives often cited by CEOs — greater customer intimacy, increasing competitive advantage and improving efficiency. CIOs should look for cloud SaaS applications that apply AI to these areas. Greater experience with AI solutions will help CIOs to build business cases and identify the limitations in current-generation technologies to understand skills needed to fill talent gaps.

“What matters the most is where your business should use AI,” says Andrews. “If you’re interested in exploring AI, the most important first step is to pursue something that is critical to your organization.”

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AI basics

Common definitions of AI focus on automation and, as a result, often miss the hidden opportunities available to IT and business leaders. AI is technology that emulates human performance, typically by learning from it.

CIOs should look for critical business points where human interaction or human expertise adds value

The most common mistake with AI is to focus on automation rather than augmentation of human decision making and interactions. If CIOs focus only on further automation via AI, they also miss the hidden opportunities for greater personalization and differentiation. AI can augment humans, as it has the ability to classify information and make predictions faster and at higher volumes than humans can accomplish on their own.

CIOs should look for critical business points where human interaction or human expertise adds value. They then should consider how AI might augment those efforts to create even more value.

Avoid the hype

Hype isn’t always a bad thing. Within limits, it fosters attention, and triggers innovation and potential investment. A little bit of hype can build excitement about potential, while too much may lead to false hopes and misguided planning assumptions.

How to sort the AI hype from reality

Although AI offers exciting possibilities, the huge increase in startups and established vendors claiming to offer AI products without any real differentiation has confused potential buyers and obfuscated the value of more straightforward, proven approaches.

CIOs have to distinguish between faux and real AI offerings

“A vendor showed us a chatbot that was intended to provide a useful dialogue between a customer and a retail company regarding the products it has on consignment,” says Andrews. “However, when we inquired about how the chatbot would improve its own conclusions from subsequent data, or from the customers’ choices, the vendor indicated that the system was based entirely on its own rules, which were regularly updated manually.”

This might resolve a business challenge, but it’s not AI.

As AI accelerates up the Hype Cycle with the promise to change business forever, CIOs have to distinguish between faux and real AI offerings. One way to do this is by asking a vendor to describe the analytical model used in its AI solution and, from there, infer how well the solution might perform in a given situation. Ask how the system learns and listen closely for indicators of self-learning, necessary training by humans or just fancy “rules” that have to be manually changed.

3 questions to ask vendors

Determine these three things when questioning a vendor:

What AI learning method is it proposing to use in its solution?

What specific skills and level of experience are needed to be successful?

How much training data is needed to “prime” the solution, and how often it will need to be retrained?

The answers to these questions go well beyond a traditional “demo.” Companies must understand how a vendor’s product uses AI and whether it would work well with the data and processes that they already possess.

Another factor to consider is the reason behind having AI in a product, as it introduces risks, complexity and costs.

AI systems are not static and require vendors to be fully invested in improving their flexibility and resilience

Consequently, any vendor claiming that its product includes AI should also be able to explain how it will benefit the end user more than versions without AI. Go beyond verifying that AI makes the product better, and get a sense of how a given vendor’s AI-enabled product is superior to others in the market.

When comparing different AI products, ask vendors how they manage risk with their AI products, and how that surpasses their competitors’ means of doing so. This is particularly important, as many vendors do not understand the risks involved in using AI.

AI systems are not static and require vendors to be fully invested in improving their flexibility and resilience. Find out what vendors are doing to improve their offerings, whether by collaborating with independent data scientists or being active players in the industry. Cloud SaaS deployment facilitates continuous innovation from a vendor, and potentially other participants in the shared environment.

Moving forward with AI

Keep these considerations in mind as you adopt AI for critical business priorities:

Look for ideas and possibilities in areas you couldn’t approach before because you didn’t have or couldn’t attract enough talented people.

Learn the lessons that are unique to your organization and minimize those that are more mainstream in nature.

Survey and engage your highest-value workers about mundane aspects of their roles that can be addressed through AI.